Extreme precipitation prediction method, device, electronic device and storage medium

Abstract

The present invention relates to the technical field of data prediction and processing, and in particular to an extreme precipitation prediction method, device, electronic device and storage medium, wherein the method comprises: obtaining historical atmospheric reanalysis data of a target area; determining the scale coefficients of extreme precipitation and near-ground temperature at each grid point in the target area according to the historical atmospheric reanalysis data, and identifying the thermal term and dynamic term of the scale coefficient; predicting the future temperature change value of the future period according to the historical atmospheric reanalysis data, predicting the initial value of extreme precipitation at each grid point in the target area in the future period based on the thermal term and dynamic term of the scale coefficient and the future temperature change value; constructing an emergence constraint model based on the historical atmospheric reanalysis data and the initial value of extreme precipitation, and the emergence constraint model outputs the prediction result of extreme precipitation in the target area in the future period. Thus, the problem of low accuracy in predicting future extreme precipitation in the related art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data prediction and processing, and in particular to an extreme precipitation prediction method, device, electronic equipment and storage medium. Background Art

[0002] Precipitation is a key factor in the hydrological cycle. In the past few decades, the intensity and frequency of extreme precipitation in most parts of the world have increased significantly, and the flood disasters caused by them have caused a lot of economic losses. The water vapor content in the atmospheric boundary layer is the material basis for the formation of precipitation. According to the CC (Clausius-Clapeyron) thermodynamic equation, the saturated water vapor pressure increases nonlinearly with the increase in temperature, which is about 6.8% / ℃, generally referred to as the CC scale. At present, some studies have further considered the physical connection between water vapor content and precipitation intensity, evaluated the intensity of global precipitation response to climate warming, and found that extreme precipitation in most regions presents a sub-CC scale or negative response coefficient. At the same time, ground observations and satellite inversion data in most parts of the world confirm that extreme precipitation generally presents a Hook climate response structure, that is, a "parabolic" trajectory that first rises and then falls with rising temperatures.

[0003] Related technologies mainly predict future extreme precipitation based on statistical methods or physical models, but do not take into account the evolution trend of extreme precipitation driven by thermal effects and the impact of Hook structure on future extreme precipitation. There are few factors considered, resulting in low accuracy in extreme precipitation prediction. Summary of the invention

[0004] The present invention provides an extreme precipitation prediction method, device, electronic device and storage medium to solve the problem of low accuracy in predicting future extreme precipitation in related technologies.

[0005] A first aspect of the present invention provides an extreme precipitation prediction method, comprising the following steps: acquiring historical atmospheric reanalysis data of a target area; determining the scaling coefficients of extreme precipitation and near-ground temperature at each grid point in the target area based on the historical atmospheric reanalysis data, and identifying the thermal term and dynamic term of the scaling coefficient; predicting future temperature change values ​​for a future period based on the historical atmospheric reanalysis data, and predicting the initial values ​​of extreme precipitation at each grid point in the target area for the future period based on the thermal term and dynamic term of the scaling coefficient and the future temperature change value; constructing an emergence constraint model based on the historical atmospheric reanalysis data and the initial values ​​of extreme precipitation, and the emergence constraint model outputs the extreme precipitation prediction results for the target area for the future period.

[0006] Optionally, the scaling coefficients of extreme precipitation and near-ground temperature in each grid point in the target area are determined based on historical atmospheric reanalysis data, including: identifying near-surface meteorological data in the historical atmospheric reanalysis data; inputting temperature data and precipitation data in the near-surface meteorological data into a box element scaling function, and the box element scaling function outputs the scaling coefficients of extreme precipitation and near-ground temperature in each grid point in the target area.

[0007] Optionally, the bin scaling function includes multiple bins with the same sample capacity, and the data processing flow of the bin scaling function includes: combining the temperature data and the precipitation data according to the data time to obtain combined data, wherein the combined data includes multiple groups of data, and each group of data includes the temperature data and precipitation data of the same day; dividing the multiple groups of data of the combined data into different bins, determining the temperature data of the corresponding bin according to the temperature data of each group of data, and determining the extreme precipitation data of the corresponding bin according to the precipitation data of each group of data; constructing a response function of extreme precipitation to temperature variation according to the temperature data and extreme precipitation data of each bin, and determining the climate response type corresponding to the extreme precipitation according to the fitting of the response function; selecting a target bin from multiple bins, and determining the scaling coefficients of extreme precipitation and near-ground temperature in each grid point in the target area according to the data and climate response type of the target bin.

[0008] Optionally, identifying thermal and dynamic terms of the scale coefficient includes: identifying meteorological data of different pressure layers in historical atmospheric reanalysis data; and determining the thermal and dynamic terms of the scale coefficient of each grid point based on wind speed data and humidity data in the meteorological data of different pressure layers.

[0009] Optionally, the initial value of extreme precipitation at each grid point in the target area in the future time period is predicted based on the thermal term and the dynamic term of the scaling coefficient and the future temperature change value, including: predicting the initial value of extreme precipitation at each grid point in the future time period based on the thermal term of the future temperature change value and the scaling coefficient; correcting the initial value of extreme precipitation according to the future temperature change value and the dynamic term of the scaling coefficient, to obtain the initial value of extreme precipitation at each grid point in the target area in the future time period.

[0010] Optionally, an emergence constraint model is constructed based on historical atmospheric reanalysis data and initial values ​​of extreme precipitation, including: identifying meteorological data output by global climate models for each grid point in the historical atmospheric reanalysis data; searching for each grid point and its corresponding adjacent grid points, calculating the historical average annual temperature change trend of each grid point in each global climate model, and integrating the historical average annual temperature change trend of the adjacent grid points corresponding to each grid point under all global models; integrating the historical average annual temperature change trends of all global models for each grid point and its adjacent grid points corresponding to each grid point to obtain multiple groups of historical average annual temperature change trends; and constructing an emergence constraint model based on multiple groups of historical average annual temperature change trends and initial values ​​of extreme precipitation.

[0011] Optionally, the emergence constraint model is:

[0012] ;

[0013] in, is the initial value of extreme precipitation, are the trends of multiple historical annual average temperatures. and are the parameters of the emergence constraint model.

[0014] A second aspect of the present invention provides an extreme precipitation prediction device, including: an acquisition module for acquiring historical atmospheric reanalysis data of a target area; a determination module for determining the scaling coefficients of extreme precipitation and near-ground temperature at each grid point in the target area based on the historical atmospheric reanalysis data, and identifying the thermal term and dynamic term of the scaling coefficient; a prediction module for predicting future temperature change values ​​for a future period based on the historical atmospheric reanalysis data, and predicting the initial values ​​of extreme precipitation at each grid point in the target area in the future period based on the thermal term and dynamic term of the scaling coefficient and the future temperature change value; a construction module for constructing an emergence constraint model based on the historical atmospheric reanalysis data and the initial values ​​of extreme precipitation, and the emergence constraint model outputs the extreme precipitation prediction results for the target area in the future period.

[0015] Optionally, the determination module is further used to: identify near-surface meteorological data in historical atmospheric reanalysis data; input temperature data and precipitation data in the near-surface meteorological data into a box-element scaling function, and the box-element scaling function outputs scaling coefficients of extreme precipitation and near-surface temperature in each grid point in the target area.

[0016] Optionally, the bin scaling function includes multiple bins with the same sample capacity, and the data processing flow of the bin scaling function includes: combining the temperature data and the precipitation data according to the data time to obtain combined data, wherein the combined data includes multiple groups of data, and each group of data includes the temperature data and precipitation data of the same day; dividing the multiple groups of data of the combined data into different bins, determining the temperature data of the corresponding bin according to the temperature data of each group of data, and determining the extreme precipitation data of the corresponding bin according to the precipitation data of each group of data; constructing a response function of extreme precipitation to temperature variation according to the temperature data and extreme precipitation data of each bin, and determining the climate response type corresponding to the extreme precipitation according to the fitting of the response function; selecting a target bin from multiple bins, and determining the scaling coefficients of extreme precipitation and near-ground temperature in each grid point in the target area according to the data and climate response type of the target bin.

[0017] Optionally, the determination module is further used to: identify meteorological data of different pressure layers in historical atmospheric reanalysis data; and determine thermal and dynamic terms of the scaling coefficient of each grid point based on wind speed data and humidity data in the meteorological data of different pressure layers.

[0018] Optionally, the prediction module is further used to: predict the initial value of extreme precipitation at each grid point in the future period based on the thermal term of the future temperature change value and the scaling coefficient; correct the initial value of extreme precipitation according to the dynamic term of the future temperature change value and the scaling coefficient to obtain the initial value of extreme precipitation at each grid point in the target area in the future period.

[0019] Optionally, the construction module is further used to: identify meteorological data output by the global climate model for each grid point in the historical atmospheric reanalysis data; search for each grid point and the corresponding adjacent grid points, calculate the historical average annual temperature change trend of each grid point in each global climate model, and integrate the historical average annual temperature change trend of the adjacent grid points corresponding to each grid point under all global models; integrate the historical average annual temperature change trends of all global models of each grid point and the adjacent grid points corresponding to each grid point to obtain multiple groups of historical average annual temperature change trends; and construct an emergence constraint model based on multiple groups of historical average annual temperature change trends and extreme precipitation initial values.

[0020] Optionally, the emergence constraint model is:

[0021] ;

[0022] in, is the initial value of extreme precipitation, are the trends of historical annual average temperature changes for multiple groups. and are the parameters of the emergence constraint model.

[0023] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the extreme precipitation prediction method as described in the above embodiment.

[0024] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program or instructions stored thereon, and the computer program or instructions are executed by a processor to perform the extreme precipitation prediction method as described in the above embodiment.

[0025] Therefore, the present invention has at least the following beneficial effects:

[0026] The embodiment of the present invention can determine the scale coefficients of extreme precipitation and near-ground temperature of each grid point in the target area according to the historical atmospheric reanalysis data of the target area, and predict the future temperature change value of the future period according to the historical atmospheric reanalysis data, and predict the initial value of extreme precipitation of each grid point in the target area in the future period based on the thermal term and dynamic term of the scale coefficient, and the future temperature change value, and by coupling the physical mechanism of extreme precipitation, that is, determining the scale coefficient and separating the thermal term and dynamic term, the prediction of extreme precipitation can be made more accurate, and an emergence constraint model is constructed according to the historical atmospheric reanalysis data and the initial value of extreme precipitation, and the extreme precipitation prediction result of the target area in the future period is output through the emergence constraint model, so as to improve the reliability of extreme precipitation prediction, so as to provide an important and highly operable reference basis for global and regional rainstorm disaster risk assessment and early warning under the climate change scenario, and provide reference value for responding to future climate disasters and scientifically formulating emission reduction strategies. Thus, the technical problems such as low accuracy in predicting future extreme precipitation in related technologies are solved.

[0027] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0029] Figure 1 A flowchart of an extreme precipitation prediction method provided according to an embodiment of the present invention;

[0030] Figure 2 A flowchart of an extreme precipitation prediction method provided according to an embodiment of the present invention;

[0031] Figure 3 A schematic diagram of a spatial sliding window method provided according to an embodiment of the present invention;

[0032] Figure 4 An exemplary diagram of an extreme precipitation prediction device provided according to an embodiment of the present invention;

[0033] Figure 5 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0035] The following describes the extreme precipitation prediction method, device, electronic device and storage medium of the embodiment of the present invention with reference to the accompanying drawings. In view of the fact that the prediction of future extreme precipitation is mainly based on statistical methods or physical models mentioned in the background technology center, the evolution trend of extreme precipitation driven by thermal action and the influence of Hook structure on future extreme precipitation are not considered, and the factors considered are few, resulting in the problem of low prediction accuracy of extreme precipitation. The present invention provides an extreme precipitation prediction method, in which the scaling coefficient of extreme precipitation and near-ground temperature of each grid point in the target area can be determined according to the historical atmospheric reanalysis data of the target area, and the future temperature change value of the future period can be predicted according to the historical atmospheric reanalysis data, and the initial value of extreme precipitation of each grid point in the target area in the future period is predicted based on the thermal term and dynamic term of the scaling coefficient, and the future temperature change value. By coupling the physical mechanism of extreme precipitation, that is, determining the scaling coefficient and separating the thermal term and dynamic term, the prediction of extreme precipitation can be made more accurate, and an emergence constraint model is constructed according to the historical atmospheric reanalysis data and the initial value of extreme precipitation. The extreme precipitation prediction result of the target area in the future period is output through the emergence constraint model, thereby improving the reliability of extreme precipitation prediction. This solves the problem of low accuracy in predicting future extreme precipitation in related technologies.

[0036] Specifically, Figure 1 A schematic flow chart of an extreme precipitation prediction method provided in an embodiment of the present invention.

[0037] like Figure 1 As shown, the extreme precipitation prediction method includes the following steps:

[0038] In step S101, historical atmospheric reanalysis data of a target area is obtained.

[0039] Among them, the target area can be a pre-selected research area; the historical atmospheric reanalysis data are the atmospheric reanalysis data of the target area in the historical period, such as the near-surface meteorological data for 30 years from 1985 to 2014, the meteorological data of different pressure layers (such as meteorological data of 50hPa~1000hPa), the meteorological data output by the global climate model, etc. Meteorological data can also be called meteorological variables.

[0040] It is understandable that the embodiments of the present invention can obtain historical atmospheric reanalysis data of the target area to facilitate subsequent prediction of extreme precipitation in future periods.

[0041] For example, for each grid point in the study area, daily precipitation and 2-meter surface temperature in the study area from 1985 to 2014 were collected from the fifth generation atmospheric reanalysis dataset (ERA5) of the European Center for Medium-Range Weather Forecasts. In order to quantitatively separate the atmospheric thermal and dynamic effects, the hourly temperature and vertical wind speed data of 20 layers (50 hPa interval) from 50 hPa to 1000 hPa in the ERA5 dataset were used and converted into daily data with a spatial resolution of 0.5°×0.5°. In order to predict future climate scenarios, the five global climate models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL) recently released by the International Coupled Model Inter-comparison Project Phase 6 (CMIP6) were used. Compared with the earlier version (CMIP5), CMIP6 uses the matrix framework of shared socioeconomic pathways (SSP) and representative concentration pathways (RCP). The embodiment of the present invention uses the model output data of the highest greenhouse gas emission scenario SSP5-8.5. The output meteorological variables of the five global climate models include daily precipitation and temperature data, and 1985-2014 (30 years in total) is selected as the historical period; in order to predict the meteorological characteristics under future climate change scenarios, the last 30-year window of this century (2071-2100) is set as the future period.

[0042] In step S102, the scaling coefficients of extreme precipitation and near-surface temperature at each grid point in the target area are determined based on historical atmospheric reanalysis data, and the thermal and dynamic terms of the scaling coefficients are identified.

[0043] It can be understood that the target area is gridded, that is, divided into multiple grid points. The embodiment of the present invention can determine the scaling coefficients of extreme precipitation and near-ground temperature in each grid point in the target area based on historical atmospheric reanalysis data, and then identify the thermal term and dynamic term in the scaling coefficient, taking into account the impact of the thermal dynamic mechanism on precipitation prediction, so as to make the subsequent prediction of extreme precipitation more accurate.

[0044] In an embodiment of the present invention, the scaling coefficients of extreme precipitation and near-ground temperature in each grid point in the target area are determined based on historical atmospheric reanalysis data, including: identifying near-surface meteorological data in the historical atmospheric reanalysis data; inputting temperature data and precipitation data in the near-surface meteorological data into a box element scaling function, and the box element scaling function outputs the scaling coefficients of extreme precipitation and near-ground temperature in each grid point in the target area.

[0045] It can be understood that the embodiment of the present invention can identify the temperature data and precipitation data in the near-surface meteorological data in the historical atmospheric reanalysis data, and use the box element scaling function to process the temperature data and precipitation data to obtain the scaling coefficients of the extreme precipitation and the near-surface temperature in each grid point in the target area. The specific processing flow of the box element scaling function is as follows.

[0046] It should be noted that the box scaling function can better characterize the nonlinear relationship between variables and has been widely used in recent years to analyze the response characteristics of extreme precipitation to global warming.

[0047] In an embodiment of the present invention, a bin scaling function includes multiple bins with the same sample capacity, and a data processing flow of the bin scaling function includes: combining temperature data and precipitation data according to data time to obtain combined data, wherein the combined data includes multiple groups of data, and each group of data includes temperature data and precipitation data of the same day; dividing the multiple groups of data of the combined data into different bins, determining the temperature data of the corresponding bin according to the temperature data of each group of data, and determining the extreme precipitation data of the corresponding bin according to the precipitation data of each group of data; constructing a response function of extreme precipitation to temperature variation according to the temperature data and extreme precipitation data of each bin, and determining the climate response type corresponding to the extreme precipitation according to the fitting of the response function; selecting a target bin from multiple bins, and determining the scaling coefficients of extreme precipitation and near-ground temperature in each grid point in the target area according to the data and climate response type of the target bin.

[0048] Among them, the climate response types include monotonically increasing type, monotonically decreasing type and Hook structure.

[0049] It can be understood that the embodiments of the present invention can combine the temperature data and the precipitation data according to the data time to obtain the combined data, wherein the combined data includes multiple groups of data, each group of data includes the temperature data and precipitation data of the same day, and the extreme precipitation data of the corresponding box element is determined according to the precipitation data of each group of data, and then the response function of the extreme precipitation to the temperature variation is constructed according to the temperature data and the extreme precipitation data of each box element, the climate response type corresponding to the extreme precipitation is determined according to the fitting of the response function, the target box element is selected from the multiple boxes, and the scaling coefficients of the extreme precipitation and the near-ground temperature in each grid point in the target area are determined according to the data and the climate response type of the target box element.

[0050] The response function is: ; P 1 and P 2 are the extreme precipitation intensities of adjacent boxes; is the scaling factor (Scaling rate), also known as the response intensity, The temperature fluctuation.

[0051] Specifically, the embodiment of the present invention can divide the temperature and precipitation combination data of the same day in the historical period into multiple boxes with the same sample capacity, and select the median of the daily temperature to represent the box temperature, and select the average intensity of the target number of precipitations higher than the target value as the extreme precipitation. For example, the average intensity of three precipitations higher than the 99% quantile represents an extreme event, and obtain the target number of groups of temperature and extreme precipitation data, and construct a response function of extreme precipitation to temperature variation, fit the response function, determine the climate response type of extreme precipitation based on the fitting result, and determine the inflection point in the Hook structure as the peak temperature Tpp, and select the target box whose box temperature is lower than the peak temperature, and fit the temperature and precipitation data in the target box to obtain the extreme precipitation and near-ground temperature scaling coefficients of each grid point.

[0052] For example, for each grid point, the daily temperature 2m near the ground is used as the sorting criterion, and the temperature-precipitation combination series of the same day in the study period is divided into 12 boxes with the same sample size; the median of the daily temperature is taken to represent the box temperature, and the average intensity of three precipitation events above the 99% quantile is used to represent the extreme events, finally obtaining 12 groups of temperature-extreme precipitation series.

[0053] The CC equation can quantitatively describe the saturated water vapor pressure With temperature T The nonlinear relationship:

[0054] ; (1)

[0055] in, L v is the latent heat of vaporization, which is 2.5×10 6 J kg -1 ; R v is the water vapor gas constant, which is 461 J kg -1 K -1 .

[0056] Atmospheric water vapor pressure is the material basis for the formation of precipitation and changes with temperature. T The increase is nonlinear; extreme precipitation and temperature may also have a similar exponential relationship, so the following formula is often used to measure the impact of extreme precipitation on temperature variation The response function is:

[0057] ; (2)

[0058] in, P 1 and P 2 are the extreme precipitation intensities of adjacent boxes; Scaling rate, also known as response intensity.

[0059] Fitting the equation via a locally weighted sliding regression smoothing method It is found that the climate response types of extreme precipitation can be divided into monotonically rising type, monotonically falling type and Hook structure (the inflection point is defined as the peak temperature, T pp ). The present invention first identifies the climate response type and T pp , and then the scaling coefficient is derived by the least squares method.

[0060] Derivation of the scaling factor is a conventional technique in the art. In this process, only the temperature below T pp The temperature-precipitation data in the box element of the target area is obtained by using the above method to obtain the scaling coefficient of each grid point in the target area.

[0061] In an embodiment of the present invention, identifying the thermal term and the dynamic term of the scale coefficient includes: identifying the meteorological data of different pressure layers in the historical atmospheric reanalysis data; and determining the thermal term and the dynamic term of the scale coefficient of each grid point according to the wind speed data and the humidity data in the meteorological data of the different pressure layers.

[0062] It can be understood that the embodiments of the present invention can identify the meteorological data of different pressure layers in the historical atmospheric reanalysis data, and determine the thermal term and dynamic term of the scale coefficient of each grid point based on the wind speed data and humidity data in the meteorological data of different pressure layers, and improve the accuracy of the prediction by coupling the physical mechanism of extreme precipitation changes, that is, separating the dynamic term and the thermal term.

[0063] For example, the thermal term of the scaling coefficient of each grid point is determined based on the temperature and precipitation data and the atmospheric column dry static energy balance equation, and the dynamic term of the scaling coefficient of each grid point is determined based on the wind speed data, humidity data and the moisture balance equation.

[0064] It should be noted that the occurrence of extreme precipitation usually means sufficient water vapor and unstable atmospheric stratification. Unstable atmospheric stratification involves the interaction of complex thermal and dynamic processes, and is related to multiple meteorological factors such as relative humidity, vertical wind speed and convective effective potential energy. Therefore, the embodiment of the present invention can separate the thermal term and dynamic term of the scaling coefficient to improve the accuracy of subsequent extreme precipitation predictions.

[0065] Specifically, the dry static energy budget of the atmospheric column is expressed as follows:

[0066] ; (3)

[0067] in, Characterizes the dry static energy, is the specific heat of dry air, and denote gravitational acceleration and vertical height respectively; Characterize time variables; Characterize the mixing ratios of rain, cloud droplets, snow, graupel and cloud ice respectively; represents the latent heat of sublimation, P to provide precipitation to the ground; Characterizes the mass-weighted integral in the vertical direction.

[0068] Extreme precipitation events are usually accompanied by strong atmospheric upward motion, so the dry static energy can be approximately represented by the vertical advection term, that is, ,in, is the vertical wind speed; assuming that the average atmospheric lapse rate is close to the wet adiabatic process, the energy conservation law gives:

[0069] ; (4)

[0070] in, represents saturated specific humidity, which is determined by air temperature and pressure to seek; , is the ratio of the gas constants of dry air and water vapor, taken as 0.622.

[0071] neglect and The difference between P e is the extreme precipitation intensity, combining the above two formulas to obtain:

[0072] ; (5)

[0073] Defining precipitation efficiency considering water balance , the above formula can be simplified to:

[0074] ; (6)

[0075] in, .

[0076] In order to study the different impact mechanisms of thermal and dynamic effects of the climate system on extreme precipitation, the change of precipitation efficiency is often ignored, and the following relationship is obtained using atmospheric static balance:

[0077] ; (7)

[0078] Further broken down into:

[0079] ; (8)

[0080] The atmospheric thermal effect affects the saturated water vapor pressure and is only controlled by the temperature and pressure. The atmospheric dynamic effect affects the circulation intensity by changing the vertical wind speed, so formula (8) can be decomposed into the thermal term and dynamic term of extreme precipitation. The present invention uses 20 layers of data from 50hPa to 1000hPa in the ERA5 data set to calculate the vertical integral of formulas (5)-(8). The above method is used to obtain the thermal term and dynamic term of each grid point in the target area.

[0081] In step S103, the future temperature change value in the future period is predicted based on the historical atmospheric reanalysis data, and the initial value of extreme precipitation at each grid point in the target area in the future period is predicted based on the thermal term and dynamic term of the scaling coefficient and the future temperature change value.

[0082] It is understandable that the embodiments of the present invention can predict future temperature change values ​​based on historical atmospheric reanalysis data. ΔT , and based on the thermal and dynamic terms of the scaling coefficient and the future temperature change value, the initial value of extreme precipitation at each grid point in the target area in the future period is predicted, as follows.

[0083] It should be noted that for each grid point, the obtained scaling factor ( ) and the thermal and dynamic terms of each grid point to obtain the thermal term of the extreme precipitation scaling coefficient ( ), as follows:

[0084] ; (9)

[0085] In an embodiment of the present invention, the initial value of extreme precipitation at each grid point in the target area in the future period is predicted based on the thermal term and the dynamic term of the scaling coefficient and the future temperature change value, including: predicting the initial value of extreme precipitation at each grid point in the future period based on the thermal term of the future temperature change value and the scaling coefficient; correcting the initial value of extreme precipitation according to the future temperature change value and the dynamic term of the scaling coefficient to obtain the initial value of extreme precipitation at each grid point in the target area in the future period.

[0086] It can be understood that the embodiment of the present invention can obtain the temperature change value of each grid point in the target area in the future period relative to the historical period, and predict the initial value of extreme precipitation of each grid point in the future period based on the thermal term of the future temperature change value and the scaling coefficient, and correct the initial value of extreme precipitation according to the dynamic term of the future temperature change value and the scaling coefficient to obtain the final initial value of extreme precipitation.

[0087] Specifically, at each grid point, for each global climate model under the SSP585 pathway, the temperature change in the future period relative to the historical period is calculated, denoted as Δ T; and calculate the extreme precipitation values ​​of the global climate model output in the historical period, using the 99% quantile of all daily precipitation series in the historical period (denoted as ).

[0088] Since the atmospheric thermal effect caused by rising temperature is an important mechanism driving the increase of extreme precipitation, Δ T The thermal terms for predicting future extreme precipitation are as follows:

[0089] ; (10)

[0090] in, represents the thermal term of extreme precipitation in the future period at this grid point, and this thermal term is used as the initial value of extreme precipitation in the future period, that is, Expressed as the initial value of extreme precipitation.

[0091] In step S104, an emergence constraint model is constructed based on historical atmospheric reanalysis data and the initial value of extreme precipitation, and the emergence constraint model outputs the extreme precipitation prediction result of the target area in the future period.

[0092] It can be understood that the embodiment of the present invention can construct an emergence constraint model based on historical atmospheric reanalysis data and extreme precipitation initial values. The emergence constraint model outputs the extreme precipitation prediction results of the target area in the future period, thereby realizing the prediction of extreme precipitation in the future period and improving the accuracy of extreme precipitation prediction.

[0093] In an embodiment of the present invention, an emergence constraint model is constructed based on historical atmospheric reanalysis data and initial values ​​of extreme precipitation, including: identifying meteorological data output by a global climate model at each grid point in the historical atmospheric reanalysis data; searching for each grid point and its corresponding adjacent grid points, calculating the historical average annual temperature change trend of each grid point in each global climate model, and integrating the historical average annual temperature change trend of the adjacent grid points corresponding to each grid point under all global models; integrating the historical average annual temperature change trends of each grid point and its corresponding adjacent grid points under all global models to obtain multiple groups of historical average annual temperature change trends; and constructing an emergence constraint model based on multiple groups of historical average annual temperature change trends and initial values ​​of extreme precipitation.

[0094] Among them, the emergence constraint model is:

[0095] ; (11)

[0096] in, is the initial value of extreme precipitation, are the trends of multiple historical annual average temperatures. and are the parameters of the emergence constraint model.

[0097] It can be understood that the embodiments of the present invention can identify the meteorological data output by the global climate model of each grid point in the historical atmospheric reanalysis data, search for each grid point and the corresponding adjacent grid points, calculate the historical average annual temperature change trend of each grid point under each global climate model, and integrate the historical average annual temperature change trend of the adjacent grid points corresponding to each grid point under all global models, integrate the historical average annual temperature change trend of each grid point and the adjacent grid points corresponding to each grid point under all global models, obtain multiple groups of historical average annual temperature change trends, and then construct an emergence constraint model based on the multiple groups of historical average annual temperature change trends and the initial value of extreme precipitation.

[0098] For example, for each grid point, the spatial sliding window method is used to search for the grid point and its neighboring grid points, a total of 25 grid points. For each grid point, the trend of the average annual temperature in the historical period under each global climate model is calculated (denoted as ). Furthermore, the data of 25 grid points in five global climate models were integrated to obtain 125 sets of historical annual average temperature change trends and future periods. The emergence constraint model constructed by the pairing combination is as follows:

[0099] ; (11)

[0100] in, and represents the parameters of the emergent constraint model;

[0101] The least squares method is used to solve the parameters of the emergence constraint model. and Based on the reanalysis data of the ERA5 dataset, the trend of the annual average 2m temperature during 1985-2014 was deduced, and the trend term was substituted into the constructed emergence constraint model, as follows:

[0102] ; (12)

[0103] in, represents the forecast results of extreme precipitation driven by thermal effects in the future after correction, Represents the average annual temperature trend obtained from the ERA5 dataset at this grid point.

[0104] In summary, the extreme precipitation prediction method of the embodiment of the present invention takes into account the road atmospheric thermodynamic mechanism and emergence constraints, and by coupling the physical mechanism of extreme precipitation changes (separating the dynamic term and the thermal term) and the statistical deviation correction method, provides an important and highly operational reference basis for global and regional rainstorm disaster risk assessment and early warning under the climate change scenario, and provides engineering reference value for responding to future climate disasters and scientifically formulating emission reduction strategies.

[0105] The extreme precipitation prediction method of the present invention is described below by a specific embodiment. Figure 2 As shown, the following steps are included:

[0106] Step 1: Select the study area and obtain atmospheric reanalysis data within the study area, including gridded near-surface meteorological variables, meteorological variables of different pressure layers from 50 hPa to 1000 hPa, and meteorological variables output by the global climate model.

[0107] Specifically include:

[0108] For each grid point in the study area, daily precipitation and 2-meter surface air temperature in the study area from 1985 to 2014 were collected from the fifth generation atmospheric reanalysis dataset (ERA5) of the European Centre for Medium-Range Weather Forecasts. In order to quantitatively separate the atmospheric thermal and dynamic effects, the hourly air temperature and vertical wind speed data of 20 layers (50 hPa interval) from 50 hPa to 1000 hPa in the ERA5 dataset were used and converted into daily data with a spatial resolution of 0.5°×0.5°.

[0109] To predict future climate scenarios, the five global climate models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) released recently in the sixth phase of the International Coupled Model Intercomparison Project (CMIP6) are used. Compared with the earlier version (CMIP5), CMIP6 uses the matrix framework of the Shared Socioeconomic Pathway (SSP) and the Representative Concentration Pathway (RCP). This example uses the model output data of the highest greenhouse gas emission scenario SSP5-8.5. The output variables of the five global climate models include daily precipitation and temperature data, and 1985-2014 (30 years in total) is selected as the historical period; to predict the meteorological characteristics under the future climate change scenario, the last 30-year window of this century (2071-2100) is set as the future period.

[0110] Step 2: Based on the bin scaling function, the temperature-precipitation combination on the same day in the study period is divided into 12 bins with the same sample size, and the scaling coefficients of extreme precipitation and near-surface temperature at each grid point in the study area are derived.

[0111] Specifically include:

[0112] The box scaling function can better characterize the nonlinear relationship between variables and has been widely used in recent years to analyze the response characteristics of extreme precipitation to global warming. For each grid point, the daily temperature 2m near the ground is used as the sorting criterion, and the temperature-precipitation combination series of the same day in the study period are divided into 12 boxes with the same sample size; the median of the daily temperature is taken to represent the box temperature, and the average intensity of three precipitation events above the 99% quantile is used to represent the extreme value event, and finally 12 sets of temperature-extreme precipitation series are obtained.

[0113] The CC equation can quantitatively describe the saturated water vapor pressure With temperature T The nonlinear relationship:

[0114] ; (1)

[0115] in, L v is the latent heat of vaporization, which is 2.5×10 6 J kg -1 ; R v is the water vapor gas constant, which is 461 J kg -1 K -1 .

[0116] Atmospheric water vapor pressure is the material basis for the formation of precipitation and changes with temperature. T The increase is nonlinear; extreme precipitation and temperature may also have a similar exponential relationship, so the following formula is often used to measure the impact of extreme precipitation on temperature variation The response function is:

[0117] ; (2)

[0118] in, P 1 and P 2 are the extreme precipitation intensities of adjacent boxes; Scaling rate, also known as response intensity.

[0119] By fitting equation (2) using the local weighted sliding regression smoothing method, it is found that the climate response types of extreme precipitation can be divided into monotonically rising type, monotonically falling type and Hook structure (the inflection point is defined as the peak temperature, T pp ). The present invention first identifies the climate response type and T pp , and then the scaling coefficient is derived by the least squares method.

[0120] Derivation of the scaling factor is a conventional technique in the art. In this process, only the temperature below T pp The temperature-precipitation data in the box element of the target area is obtained by using the above method to obtain the scaling coefficient of each grid point in the target area.

[0121] Step 3: Based on the clean energy budget and water balance equation of the atmospheric column, separate the thermal and dynamic terms of the extreme precipitation scaling coefficient at each grid point.

[0122] Specifically include:

[0123] The dry static energy budget of the atmospheric column is expressed as follows:

[0124] ; (3)

[0125] in, Characterizes the dry static energy, is the specific heat of dry air, and denote gravitational acceleration and vertical height respectively; Characterize time variables; Characterize the mixing ratios of rain, cloud droplets, snow, graupel and cloud ice respectively; represents the latent heat of sublimation, P to provide precipitation to the ground; Characterizes the mass-weighted integral in the vertical direction.

[0126] Extreme precipitation events are usually accompanied by strong atmospheric upward motion, so the dry static energy can be approximately represented by the vertical advection term, that is, ,in, is the vertical wind speed; assuming that the average atmospheric lapse rate is close to the wet adiabatic process, the energy conservation law gives:

[0127] ; (4)

[0128] in, represents saturated specific humidity, which is determined by air temperature and pressure to obtain; , is the ratio of the gas constants of dry air and water vapor, taken as 0.622.

[0129] neglect and The difference between P e is the extreme precipitation intensity, combining the above two formulas to get:

[0130] ; (5)

[0131] Defining precipitation efficiency considering water balance , the above formula can be simplified to:

[0132] ; (6)

[0133] in, .

[0134] In order to study the different impact mechanisms of thermal and dynamic effects of the climate system on extreme precipitation, the change of precipitation efficiency is often ignored, and the following relationship is obtained using atmospheric static balance:

[0135] ; (7)

[0136] Further broken down into:

[0137] ; (8)

[0138] The atmospheric thermal effect affects the saturated water vapor pressure and is only controlled by the temperature and pressure. The atmospheric dynamic effect affects the circulation intensity by changing the vertical wind speed, so formula (8) can be decomposed into the thermal term and dynamic term of extreme precipitation. The present invention uses 20 layers of data from 50hPa to 1000hPa in the ERA5 data set to calculate the vertical integral of formulas (5)-(8). The above method is used to obtain the thermal term and dynamic term of each grid point in the target area.

[0139] Step 4: Based on the meteorological data output by the global climate model, the thermal term of the scaling coefficient and the future temperature change value, the preliminary prediction value of extreme precipitation driven by thermal effects is predicted.

[0140] Specifically include:

[0141] For each grid point, the scaling factor obtained in step 2 is ( ), then according to the result of step 3, the thermal term of the extreme precipitation scaling coefficient is further obtained ( ), as follows:

[0142] ; (9)

[0143] At each grid point, for each global climate model under the SSP585 pathway, the temperature change in the future period relative to the historical period is calculated, denoted as Δ T ; and calculate the extreme precipitation values ​​of the global climate model output in the historical period, using the 99% quantile of all daily precipitation series in the historical period (denoted as ).

[0144] Since the atmospheric thermal effect caused by rising temperature is an important mechanism driving the increase of extreme precipitation, Δ T The thermal terms for predicting future extreme precipitation are as follows:

[0145] ; (10)

[0146] in, represents the thermal term of extreme precipitation in the future period at this grid point, and this thermal term is used as the initial value of extreme precipitation in the future period, that is, Expressed as the initial value of extreme precipitation.

[0147] Step 5: Use the temperature output by the global climate model and the preliminary prediction of extreme precipitation in step 4 to build an emergence constraint model and reduce the uncertainty of future extreme precipitation event predictions.

[0148] Specifically include:

[0149] like Figure 3 As shown in the figure, for each grid point, the spatial sliding window method is used to search for the grid point and its neighboring grid points, a total of 25 grid points. For each grid point, the trend of the historical average annual temperature in each global climate model is calculated (denoted as ). Furthermore, the data of 25 grid points in five global climate models were integrated to obtain 125 sets of historical annual average temperature change trends and future periods. The emergence constraint model constructed by the pairing combination is as follows:

[0150] ; (11)

[0151] in, and represents the parameters of the emergent constraint model;

[0152] The least squares method is used to solve the parameters of the emergence constraint model. and Based on the reanalysis data of the ERA5 dataset, the trend of the annual average 2m temperature during 1985-2014 was deduced, and the trend term was substituted into the constructed emergence constraint model, as follows:

[0153] ; (12)

[0154] in, represents the forecast results of extreme precipitation driven by thermal effects in the future after correction, Represents the average annual temperature trend obtained from the ERA5 dataset at this grid point.

[0155] According to the extreme precipitation prediction method proposed in the embodiment of the present invention, the scaling coefficients of extreme precipitation and near-ground temperature of each grid point in the target area can be determined according to the historical atmospheric reanalysis data of the target area, and the future temperature change values ​​of the future period can be predicted according to the historical atmospheric reanalysis data. The initial values ​​of extreme precipitation of each grid point in the target area in the future period are predicted based on the thermal term and dynamic term of the scaling coefficient and the future temperature change value. By coupling the physical mechanism of extreme precipitation, that is, determining the scaling coefficient and separating the thermal term and dynamic term, the prediction of extreme precipitation can be made more accurate. An emergence constraint model is constructed according to the historical atmospheric reanalysis data and the initial value of extreme precipitation. The extreme precipitation prediction results of the target area in the future period are output through the emergence constraint model, so as to improve the reliability of extreme precipitation prediction, so as to provide an important and highly operational reference basis for global and regional rainstorm disaster risk assessment and early warning under climate change scenarios in the future, and provide reference value for responding to future climate disasters and scientifically formulating emission reduction strategies.

[0156] Next, the extreme precipitation prediction device proposed in the embodiment of the present invention is described with reference to the accompanying drawings.

[0157] Figure 4 4 is a block diagram of an extreme precipitation prediction device according to an embodiment of the present invention.

[0158] like Figure 4 As shown, the extreme precipitation prediction device 10 includes: an acquisition module 100, a determination module 200, a prediction module 300 and a construction module 400.

[0159] Among them, the acquisition module 100 is used to obtain the historical atmospheric reanalysis data of the target area; the determination module 200 is used to determine the scaling coefficients of extreme precipitation and near-ground temperature at each grid point in the target area based on the historical atmospheric reanalysis data, and identify the thermal term and dynamic term of the scaling coefficient; the prediction module 300 is used to predict the future temperature change value in the future period based on the historical atmospheric reanalysis data, and predict the initial value of extreme precipitation at each grid point in the target area in the future period based on the thermal term and dynamic term of the scaling coefficient and the future temperature change value; the construction module 400 is used to construct an emergence constraint model based on the historical atmospheric reanalysis data and the initial value of extreme precipitation, and the emergence constraint model outputs the extreme precipitation prediction result of the target area in the future period.

[0160] In an embodiment of the present invention, the determination module 200 is further used to: identify near-surface meteorological data in historical atmospheric reanalysis data; input the temperature data and precipitation data in the near-surface meteorological data into a box-cell scaling function, and the box-cell scaling function outputs the scaling coefficients of extreme precipitation and near-surface temperature in each grid point in the target area.

[0161] In an embodiment of the present invention, a bin scaling function includes multiple bins with the same sample capacity, and a data processing flow of the bin scaling function includes: combining temperature data and precipitation data according to data time to obtain combined data, wherein the combined data includes multiple groups of data, and each group of data includes temperature data and precipitation data of the same day; dividing the multiple groups of data of the combined data into different bins, determining the temperature data of the corresponding bin according to the temperature data of each group of data, and determining the extreme precipitation data of the corresponding bin according to the precipitation data of each group of data; constructing a response function of extreme precipitation to temperature variation according to the temperature data and extreme precipitation data of each bin, and determining the climate response type corresponding to the extreme precipitation according to the fitting of the response function; selecting a target bin from multiple bins, and determining the scaling coefficients of extreme precipitation and near-ground temperature in each grid point in the target area according to the data and climate response type of the target bin.

[0162] In an embodiment of the present invention, the determination module 200 is further used to: identify meteorological data of different pressure layers in historical atmospheric reanalysis data; and determine the thermal term and dynamic term of the scaling coefficient of each grid point based on the wind speed data and humidity data in the meteorological data of different pressure layers.

[0163] In an embodiment of the present invention, the prediction module 300 is further used to: predict the initial value of extreme precipitation at each grid point in the future period based on the thermal term of the future temperature change value and the scaling coefficient; correct the initial value of extreme precipitation according to the dynamic term of the future temperature change value and the scaling coefficient to obtain the initial value of extreme precipitation at each grid point in the target area in the future period.

[0164] In an embodiment of the present invention, construction module 400 is further used to: identify meteorological data output by the global climate model of each grid point in the historical atmospheric reanalysis data; search each grid point and the corresponding adjacent grid points, calculate the historical average annual temperature change trend of each grid point in each global climate model, and integrate the historical average annual temperature change trend of the adjacent grid points corresponding to each grid point under all global models; integrate the historical average annual temperature change trends of each grid point and the adjacent grid points corresponding to each grid point under all global models to obtain multiple groups of historical average annual temperature change trends; construct an emergence constraint model based on multiple groups of historical average annual temperature change trends and extreme precipitation initial values.

[0165] In an embodiment of the present invention, the emergence constraint model is:

[0166] ;

[0167] in, is the initial value of extreme precipitation, are the trends of historical annual average temperature changes for multiple groups. and are the parameters of the emergence constraint model.

[0168] It should be noted that the aforementioned explanation of the extreme precipitation prediction method embodiment is also applicable to the extreme precipitation prediction device of this embodiment, and will not be repeated here.

[0169] According to the extreme precipitation prediction device proposed in the embodiment of the present invention, the scaling coefficients of extreme precipitation and near-ground temperature of each grid point in the target area can be determined according to the historical atmospheric reanalysis data of the target area, and the future temperature change values ​​of the future period can be predicted according to the historical atmospheric reanalysis data. The initial values ​​of extreme precipitation of each grid point in the target area in the future period are predicted based on the thermal term and dynamic term of the scaling coefficient and the future temperature change value. By coupling the physical mechanism of extreme precipitation, that is, determining the scaling coefficient and separating the thermal term and dynamic term, the prediction of extreme precipitation can be made more accurate. An emergence constraint model is constructed according to the historical atmospheric reanalysis data and the initial value of extreme precipitation. The extreme precipitation prediction results of the target area in the future period are output through the emergence constraint model, so as to improve the reliability of extreme precipitation prediction, so as to provide an important and highly operational reference basis for global and regional rainstorm disaster risk assessment and early warning under climate change scenarios in the future, and provide reference value for responding to future climate disasters and scientifically formulating emission reduction strategies.

[0170] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0171] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0172] When the processor 502 executes the program, the extreme precipitation prediction method provided in the above embodiment is implemented.

[0173] Furthermore, the electronic device further comprises:

[0174] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0175] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0176] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0177] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0178] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0179] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0180] An embodiment of the present invention further provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the extreme precipitation prediction method as described above is implemented.

[0181] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0182] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0183] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0184] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0185] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

Claims

1. A method for predicting extreme precipitation, characterized in that: The following steps are involved: Obtain historical atmospheric reanalysis data for the target area; Determine the scaling coefficients of extreme precipitation and near-ground temperature at each grid point in the target area according to the historical atmospheric reanalysis data, and identify the thermal term and dynamic term of the scaling coefficient; predict the future temperature change value in the future period according to the historical atmospheric reanalysis data, and predict the initial value of extreme precipitation at each grid point in the target area in the future period based on the thermal term and dynamic term of the scaling coefficient and the future temperature change value; The identifying of the thermal term and the dynamic term of the scaling coefficient comprises: identifying the meteorological data of different pressure layers in the historical atmospheric reanalysis data; determining the thermal term and the dynamic term of the scaling coefficient of each grid point according to the wind speed data and the humidity data in the meteorological data of the different pressure layers; An emergence constraint model is constructed based on the historical atmospheric reanalysis data and the initial value of extreme precipitation, and the emergence constraint model outputs the extreme precipitation prediction result of the target area in the future period; the construction of the emergence constraint model based on the historical atmospheric reanalysis data and the initial value of extreme precipitation includes: identifying the meteorological data output by the global climate model of each grid point in the historical atmospheric reanalysis data; searching for each grid point and the corresponding adjacent grid points, and calculating the historical average annual temperature change trend of each grid point under each global climate model; integrating the historical average annual temperature change trends of all global models of each grid point and the adjacent grid points corresponding to each grid point to obtain multiple groups of historical average annual temperature change trends; constructing the emergence constraint model based on the multiple groups of historical average annual temperature change trends and the initial value of extreme precipitation; the emergence constraint model is: ； in, is the initial value of extreme precipitation, are the trends of historical annual average temperature changes for multiple groups. and are the parameters of the emergence constraint model.

2. The extreme precipitation prediction method according to claim 1, characterized in that: Determining the scaling coefficients of extreme precipitation and near-ground temperature at each grid point in the target area according to the historical atmospheric reanalysis data includes: Identify near-surface meteorological data in the historical atmospheric reanalysis data; The temperature data and precipitation data in the near-surface meteorological data are input into a box-cell scaling function, and the box-cell scaling function outputs the scaling coefficients of the extreme precipitation and the near-surface temperature in each grid point in the target area.

3. The extreme precipitation prediction method according to claim 2, characterized in that: The bin scaling function includes a plurality of bins with the same sample capacity, and the data processing flow of the bin scaling function includes: Combining the temperature data and the precipitation data according to the data time to obtain combined data, wherein the combined data includes multiple groups of data, each group of data includes temperature data and precipitation data of the same day; Dividing the multiple groups of data of the combined data into different boxes, determining the temperature data of the corresponding box according to the air temperature data of each group of data, and determining the extreme precipitation data of the corresponding box according to the precipitation data of each group of data; Constructing a response function of extreme precipitation to temperature variation based on the temperature data and extreme precipitation data of each box, and determining the climate response type corresponding to extreme precipitation based on the fitting of the response function; A target box is selected from a plurality of boxes, and scaling coefficients of extreme precipitation and near-ground temperature in each grid point in the target area are determined according to data of the target box and the climate response type.

4. The extreme precipitation prediction method according to claim 1, characterized in that: The method of predicting the initial value of extreme precipitation at each grid point in the target area in the future period based on the thermal term and the dynamic term of the scaling coefficient and the future temperature change value includes: Based on the future temperature change value and the thermal term of the scaling coefficient, predict the initial value of extreme precipitation at each grid point in the future period; The extreme precipitation initial value is corrected according to the future temperature change value and the dynamic term of the scaling coefficient to obtain the extreme precipitation initial value of each grid point in the target area in the future period.

5. An extreme precipitation prediction device, characterized in that: include: The acquisition module is used to obtain historical atmospheric reanalysis data of the target area; A determination module, for determining the scaling coefficients of extreme precipitation and near-surface temperature at each grid point in the target area according to the historical atmospheric reanalysis data, and identifying the thermal term and dynamic term of the scaling coefficient; A prediction module, used to predict the future temperature change value in the future period according to the historical atmospheric reanalysis data, and predict the initial value of extreme precipitation at each grid point in the target area in the future period based on the thermal term and dynamic term of the scaling coefficient and the future temperature change value; The identifying of the thermal term and the dynamic term of the scaling coefficient comprises: identifying the meteorological data of different pressure layers in the historical atmospheric reanalysis data; determining the thermal term and the dynamic term of the scaling coefficient of each grid point according to the wind speed data and the humidity data in the meteorological data of the different pressure layers; A construction module is used to construct an emergence constraint model based on the historical atmospheric reanalysis data and the extreme precipitation initial value, and the emergence constraint model outputs the extreme precipitation prediction result of the target area in the future period; the construction of the emergence constraint model based on the historical atmospheric reanalysis data and the extreme precipitation initial value includes: identifying the meteorological data output by the global climate model of each grid point in the historical atmospheric reanalysis data; searching for each grid point and the corresponding adjacent grid points, and calculating the historical average annual temperature change trend of each grid point under each global climate model; integrating the historical average annual temperature change trends of all global models of each grid point and the adjacent grid points corresponding to each grid point to obtain multiple groups of historical average annual temperature change trends; constructing the emergence constraint model based on the multiple groups of historical average annual temperature change trends and the extreme precipitation initial value; the emergence constraint model is: ； in, is the initial value of extreme precipitation, are the trends of historical annual average temperature changes for multiple groups. and are the parameters of the emergence constraint model.

6. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the extreme precipitation prediction method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: The computer program or instruction is executed by a processor to implement the extreme precipitation prediction method according to any one of claims 1 to 4.

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